Data In Wireless Sensor Networks Research Articles

Energy-Aware and Density-Based Clustering and Relaying Protocol (EA-DB-CRP) for gathering data in wireless sensor networks

In this paper, we propose a novel energy-aware and density-based clustering and routing protocol (EA-DB-CRP) for gathering data in wireless sensor networks which basically aims at distributing the load among available sensor nodes which in turn balances the energy consumption in the network and consequently elongates the network lifetime. More precisely, we introduce a network model that ends up of having empirical expressions that describe how to partition the network field efficiently into equal-size layers and sub-layers. In each sub-layer, the role of cluster head is pivoted among all cluster individuals, in a round robin fashion, that are sorted in a list in a descending order based on an extremely effective cluster head weight. Additionally, there are a minimum number of cluster members maintained to guarantee the feasibility of clusters being formed and this is through proposing a cluster merge algorithm. Not only to this extent, but rather, we maintain the consideration of network density over created clusters to balance them and subsequently prolong the network lifetime. Lastly, an effective relaying algorithm is proposed in which cluster heads get aware of those sensor nodes located in a layer ahead toward the base station along with their relay-node weights whereas each cluster head picks the relay node that has the highest weight. Our proposed protocol is evaluated through conducting various MATLAB simulations. Strikingly, results demonstrated that our proposed protocol has a momentous change against other related works regarding network lifetime and energy use.

Intelligent Network Security Monitoring Based on Optimum-Path Forest Clustering

Distinguishing outliers from normal data in wireless sensor networks has been a big challenge in the anomaly detection domain, mostly due to the nature of the anomalies, such as software or hardware failures, reading errors or malicious attacks, just to name a few. In this article, we introduce an anomaly detection-based OPF classifier in the aforementioned context. The results are compared against one-class support vector machines and multivariate Gaussian distribution. Additionally, we also propose to employ meta-heuristic optimization techniques to finetune the OPF classifier in the context of anomaly detection in wireless sensor networks.

The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data.

Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to extract features from missing or corrupted data, so incomplete data are widely used in practical work. In this paper, the optimally designed variational autoencoder networks is proposed for extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM) to improve cluster performance of incomplete data. Specifically, the feature extraction model is improved by using variational autoencoder to learn the feature of incomplete data. To capture nonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm is used to cluster low-dimensional features. The tensor distance is used as the distance measure to capture the unknown correlations of data as much as possible. Finally, in the case that the clustering results are obtained, the missing data can be restored by using the low-dimensional features. Experiments on real datasets show that the proposed algorithm not only can improve the clustering performance of incomplete data effectively, but also can fill in missing features and get better data reconstruction results.

RETRACTED ARTICLE: Research on distributed localization algorithm for wireless sensor network nodes in multimedia data transmission process

Aiming at the need of massive transmission bandwidth and efficient transmission efficiency in the transmission of multimedia data in wireless sensor networks, this paper proposes an improved BP neural network localization algorithm based on artificial bee colony algorithm to solve the problem of network node localization in the transmission of multimedia data in wireless sensor networks. The algorithm first completes the coarse location of the nodes by the three side distributed location algorithm through signal strength. Then, the BP neural network is optimized by artificial bee colony to locate accurately. The distance weight is determined based on the precise location and the errors generated in the process of ranging are corrected. Finally, a localization algorithm with high accuracy and robustness for wireless sensor network nodes is formed. The simulation results show that the proposed algorithm has a significant improvement in the performance and efficiency of sensor node positioning, and has a broad prospect in the transmission of multimedia data.

RETRACTED ARTICLE: Data cleansing method of talent management data in wireless sensor network based on data mining technology

Data mining technology is a very common computer technology, which has been widely used in many fields because of its superior performance. The method of talent management data cleaning in wireless sensor networks is studied based on data mining technology. The research status of data mining technology is first introduced at home and abroad, and the specific application forms of wireless sensor networks are analyzed. Then, the structure characteristics of wireless sensor networks are introduced, and a data cleansing technology is proposed based on clustering model. A cluster-based replication record deletion algorithm is proposed, and finally, the accuracy of data cleansing methods is verified. The results show that the research method of this paper is correct and effective.

A review of methods of embedding information in digital objects for security in the internet of things

Transmission, processing and storage of information in the infrastructure of the Internet of Things are related to the necessity for solving a number of problems in information security. The main difficulty lies in the fact that the infrastructure of the Internet of Things is not homogeneous and contains many different devices, including those with limited computing resources. One of the approaches to solving these problems is to embed additional information into the transmitted and stored digital objects. In this paper we present a review of methods of embedding information in digital data to provide security in the Internet of Things, including methods of steganographic embedding of information and methods for embedding digital watermarks. We reviewed methods of embedding information into digital images, as well as wireless sensor network data, proposed for use in the Internet of Things. In this paper we defined the advantages and disadvantages of individual methods and groups of methods, also we analyzed their applicability for data protection in the Internet of Things. Relevant trends in this field of research have been identified.

Joint Channel Assignment and Stochastic Energy Management for RF-Powered OFDMA WSNs

As an emerging energy replenishment technique, radio-frequency (RF) energy transfer has been considered as a promising solution to power the low-end wireless sensor networks (WSNs). However, in RF-powered orthogonal frequency-division multiple accessing (OFDMA) WSNs, both the energy provisioning and data transmission are easily impacted by the highly dynamic and unpredictable wireless channels, making channel assignment and energy management closely coupled. Furthermore, due to the lack of priori knowledge of stochastic channel conditions, network performance optimization becomes more challenging. In this paper, we investigate joint channel assignment and stochastic energy management to optimize the long-term network utility in RF-powered OFDMA WSNs, by jointly considering the stochasticity in RF energy harvesting rates and channel fading. We formulate the network utility optimization problem (NUOP) as a mixed-integer non-linear stochastic optimization problem. By leveraging Lyapunov optimization to decouple NUOP into two subproblems and address them separately, we propose an online algorithm, named JAMA, to adaptively adjust the sampling rates, data transmission power, transmission rate, and channel assignment according to the ever-changing channel conditions, while guarantee the network stability and sustainability. In addition, theoretical performance analysis is provided to prove that JAMA can achieve near-optimal network utility and satisfy the network stability and sustainability constraints. Extensive simulation results based on the experimental data of an RF-powered WSN testbed verify the effectiveness and efficiency of JAMA and evaluate the impacts of system parameters on network performance.

Compressive Sparse Data Gathering With Low-Rank and Total Variation in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been deeply studied by many researchers and been widely used in many fields. Since a large amount of energy for WSNs is used for sensing and transmitting, researchers come up with many methods to reduce the number of sensed and transmitted data packets. Compressive Data Gathering (CDG) is a well-known method to gather WSNs data, but it does not realize sparse sensing as it needs to sense all data and compress them. The efficiency of Low-rank and TV regularizations for recovering WSNs data has been demonstrated, however, they are not combined to enable utilization of data correlation throughout the network. To recover the data accurately and to reduce the energy consumption in WSNs, we propose a Compressive Sparse Data Gathering (CSDG) scheme including a Compressive Sparse Sampling (CSS) method and a data recovery algorithm based on low-rank and Total Variation (TV) regularizations fully exploiting the sparsity and low-rank characteristics of WSNs data. The alternating direction method of multipliers and the steepest descent method are used to solve the problem. Simulations show that the CSDG method outperforms the state-of-the-art methods in terms of the recovery accuracy. Moreover, with fairly low sparse sampling ratio and high compression ratio, CSDG method can still recover the original signal with little error. As the number of sensed data and transmitted data is reduced greatly with sparse sampling and compression, the energy consumption of WSNs is lessen and the lifetime is prolonged.

An Energy Aware Adaptive Kernel Density Estimation Approach to Unequal Clustering in Wireless Sensor Networks

Energy conservation is one of the most important challenges in wireless sensor networks (WSNs). Therefore, compared with the traditional networks, the WSNs not only need high-quality services with high throughput or low transmission delay, but also pay greater attention to energy utilization to extend network lifetime. The clustering routing algorithm is considered to be among the effective ways to collect and transmit data in WSNs. Cluster head (CH) plays a vital role in the cluster which is in charge of data aggregation and data transmission, so their energy consumption is higher than non-CH nodes. The traditional clustering algorithm tends to have the same size in each cluster. However, due to the randomness of the node distribution, the equal clustering mechanism obviously cannot reduce energy consumption. In order to solve this problem, this paper contributes a new unequal clustering algorithm, an energy-aware adaptive kernel density estimation algorithm (EAKDE), which aims to balance the energy dissipation among the CHs. EAKDE utilizes fuzzy logic to determine the priority of nodes competing for CH. In order to adapt the dynamic change of node conditions, adaptive kernel density estimation algorithm is utilized to assign the appropriate unequal cluster radius to sensor nodes. The simulation results demonstrate that, in different scenarios, EAKDE outperforms the other well-known algorithms in terms of network stability, network lifetime, and energy efficiency.

Privacy Preserving Data Mining Approach for IoT based WSN in Smart City

Wireless Sensor Network (WSN) is one of the most fundamental technologies of Internet of Things (IoT). Various IoT devices are connected to the internet by making use of WSN composed of different sensor nodes and actuators, where these sensor nodes collaborate and accomplish their tasks dynamically. The main objective of deploying WSN-based applications is to make high precision real-time observations, and it is extremely challenging because of the limited computing power of the sensors operating under constrained environments, resource constraints like energy, computation speed, bandwidth and memory, huge volume of high speed, heterogeneous and fast-changing WSN data. These challenges encouraged the researchers to concentrate deeper on exploring data mining techniques to extract the required information from the fast-changing sensor data in WSN and thereby efficiently handle the massive data generated by the WSNs. The increasing need of data mining techniques for WSN has inspired us to propose a distributed data mining technique that effectively handles the data generated by the nodes in the WSN and prolongs the lifespan of the network. Our work provides a novel cluster based scheme to mine the sensors data without moving it to cluster head (CH) or base station (BS) to achieve maximum performance in a WSN environment. The basic idea of the proposed work is that local computations are performed by utilizing the computing power at each sensor node and then the minimum higher level statistical summaries are exchanged, which decreases the energy dissipation in communication as the amount of the sensor data transferred is considerably reduced, and thereby the sensor network lifetime is maximized and also preserve the privacy of the sensor data.

An Energy-Efficient Multi-Ring-Based Routing Scheme for WSNs

Data aggregation is an important solution to decrease energy consumption by reducing the transmitting data in the wireless sensor networks (WSNs). Although there have been many energy-efficient data routing algorithms devoted to data gathering, the highly correlation of sensed data in WSNs is not fully considered in many existing studies. A lot of redundant data is still transmitted to the sink, which increases the energy cost in the WSNs. In this paper, a novel scheme named Energy-efficient Routing Scheme based on Multi-Ring (ERSMR) is proposed to minimize the energy consumption. The core idea of the scheme is to aggregate data packets of nodes in the peripheral area as far as possible to achieve fully utilization of node energy far from the sink. The proposed scheme can not only minimize the size of receiving and sending data packets in hotspots, but also reduce the maximum energy consumption of nodes for prolonging the network lifetime. Furthermore, it could balance energy consumption in the network and increase energy efficiency of the nodes by exploiting the remaining energy of peripheral nodes. The correctness of the proposed scheme is proved by theory analysis. Simulations are implemented to evaluate the efficiency of the proposed scheme. The simulation results show that it could improve the network lifetime and the energy efficiency. Compared with the Data Aggregation scheme Centralized Sink (DACS), the lifetime can be increased by as much as 190%. The energy utilization efficiency can be increased by as much as 200%. In addition, the optimal transmission radius to maximize the network lifetime is described.

Multi-Attribute Data Recovery in Wireless Sensor Networks With Joint Sparsity and Low-Rank Constraints Based on Tensor Completion

In wireless sensor networks (WSNs), data recovery is an indispensable operation for data loss or energy constrained WSNs using sparse sampling. However, the recovery accuracy is not satisfying for WSNs with various sensor types due to the neglect of the correlation among multi-attribute data. In this paper, we propose a novel data recovery method with joint sparsity and low-rank constraints based on tensor completion for multi-attribute data in WSNs. The proposed method represents the high-dimensional data as low-rank tensors to effectively exploit the correlation that exists in the multi-attribute data. The utilization of the spatiotemporal sparsity in the signal is emphasized by sparsity constraints. Furthermore, an algorithm based on the alternating direction method of multipliers is developed to solve the resultant optimization problem efficiently. Experimental results demonstrate that the proposed method significantly outperforms existing solutions in terms of recovery accuracy in WSNs.

An Empirical Investigation of Securing Internet of Things Data in Wireless Sensor Network

Internet of things (IoT) is an emerging technology which can interconnect low-cost and on-demand processing resources and enabling them for transmission. They are becoming viable solutions to many challenging problems such as in environmental monitoring, business, and military applications. However, deploying without security in mind has often proved to be unreasonably dangerous. This also applies to wireless sensor network (WSN) that monitor sensitive information. The development of WSN consists of sensors to monitor IoT devices. Even the IoT technology offers a variety of services, security issues concern with devices and customers is a major problem. To handle such sensitive situations and to ensure proper data security, wireless sensor network come up with the extensive use of various methods that can bring unique security concern for customers and also provide methods to prevent various possible security attacks. For this paper, huge potential efforts are made to analyze various data security methods of IoT technology in wireless sensor networks by providing as much information possible and quantifying what the tradeoffs will be.

An Efficient Approach for Representing and Sending Data in Wireless Sensor Networks

An Efficient Approach for Representing and Sending Data in Wireless Sensor Networks

Data Fusion in Wireless Communication Network Node Positioning

Aiming at the problems of low localization accuracy, high energy consumption and delay of transmission in wireless sensor networks, a wireless sensor network data fusion method is proposed. Combined with the theory of D-S argument, the wireless sensor network system model is first constructed. Then based on the analysis of ACO (Ant Colony Optimization) and MST (Minimum Spanning Tree) methods, a wireless sensor network data fusion method is proposed. The simulation results show that the larger the number of nodes in WSN is, the larger the energy consumption and delay are. As the moving speed of Sink node increases, the average transmission delay decreases and the useful data rate increases. The average energy The consumption is also gradually increasing; the performance of proposed WSN data fusion method is better than that of ACO and MST.

Quality enhancement with fault tolerant embedding in video transmission over WMSNs in 802.11e WLAN

With the ever-growing demand in video transmissions, it is essential to design an efficient compression and powerful transmission scheme to transmit a large volume of data in wireless multimedia sensor networks (WMSNs). The main objective of this work is to maximize the network throughput and to maintain/support a better visual experience to different end-users. In this paper, we propose a predictive compression with fault-tolerant embedding based multi-path routing and concealment process to maintain the Quality of Service (QoS) and Quality of Experience (QoE) in video transmission. In order to reduce the bit rate in high-efficiency videos, a Scalable High-efficiency Inter-layer Prediction based Video coding (SHIPVC) is proposed with two layer predictions such as texture color, and motion with different quantization parameters to attain the high coding efficiency. After compression, a multi-path genetic algorithm establishes a routing and fault tolerance embedding during transmission to maintain the QoS. In decoding side, QoE is maintained by concealing the Intra frame (I-frame) and Predicted frame (P-frame) separately using the motion vector estimation. Experimental results show better performances through bit rate reduction, maximum fault tolerance, and minimum delay parameters. The PSNR calculation for concealment technique shows the better result when compared to the previous standards. Hence, the proposed method performs the effective video transmission with acceptable quality measurements in WMSNs.

Providing trusted data for industrial wireless sensor networks

The deployment of wireless sensor networks, or WSNs, in industrial domains has attracted much attention over the past few years. An increasing number of applications have been developed such as for condition monitoring in the railway industry. Nevertheless, compared with traditional WSNs, the industrial environment is harsher, noisier, and more complex, which poses a higher requirement for the network security especially in terms of data trustiness and which further deters WSN practical integration in industrial applications. The main contribution of this research is to partially address the security issues by means of providing trusted data for industrial WSNs. To this end, a negative binomial distribution-based trust scheme combined with the D–S belief theory and a noise filter method is proposed and designed for industrial WSNs. In this paper, we first discuss the trust theory in WSNs and the disadvantages of traditional trust schemes for industrial applications, then analyze and evaluate the proposed method, and finally compare the performance of our method with some classic trust schemes. Through simulation tests about temperature readings of a factory workshop, it shows that the proposed method can improve the data trustiness, reliability, and robustness in the trust evaluation process under industrial environments and ensure the security of the network.

Web Browser Based Data Visualization Scheme for XBee Wireless Sensor Network

Wireless sensor network (WSN) plays an important role in the infrastructure of Internet of Things (IoT). Data visualization is an essential component in WSN to facilitate data scientists to interpret information clearly and efficiently. In this paper, we conduct a study of XBee based WSN which integrates the DASH data visualization scheme for building a web-browser based application without using HTML or JavaScript. The data collected from wireless sensors in a WSN were displayed in a web browser with interactive functions. The proposed visualization scheme is real-time, cross platform, and hardware independent. Thus, it could be easily employed on any operating system. Experimental results demonstrated that our WSN data visualization scheme using XBee Python package and Plotly's DASH is feasible for IoT applications like smart buildings, environment monitoring, as well as other WSN applications.

An Energy Efficient Data Collection Using Multiple UAVs in Wireless Sensor Network: A Survey Study

Today, with scientific and technological advances in robotics, artificial intelligence, control and computers, land, air, and sea vehicles, they have been considered. Unmanned aerial vehicles (UAVs) have also significantly improved and are very useful for many important applications in the business, urban and military environment. One of the important uses of UAVs in Wireless Sensor Networks (WSNs) is that devices with low energy and may not be able to communicate in large areas. Nevertheless, a UAV can provide a tool for collecting the data of WSN from one device and transmitting it to another device. This article focuses on the field of research on practical applications of UAVs as mobile collectors for wireless sensor networks. First, the investigations of the proposed UAV were studied and compared their weaknesses with each other. Then, the technical challenges of the applications of UAVs in the wireless sensor network were explored.

Energy‐efficient data reconstruction algorithm for spatially‐ and temporally correlated data in wireless sensor networks

This study introduces a novel algorithm for reconstruction of wireless sensor networks data which inherently have spatial and temporal correlations. The authors' algorithm is based on compressed sensing (CS) and benefits from sliding window processing. This new algorithm rearranges the data in form of a cube and uses this representation to extract more information about the data. There are two optimisation loops which are solved simultaneously and periodically reconstruct one part of the whole signal from measurements that arrive at the sink. In particular, the first reconstruction loop, which uses a modified version of basis pursuit reconstruction algorithm, is meant for reconstruction of a temporal data which is extracted from the data cube, and the second loop which uses a modified version of reweighted l 1 -norm algorithm is for reconstruction of data windows. The authors used a special kind of binary sparse random measurement matrices for sampling which is equipped with a condition to get samples as variously as possible and this, in turn, balances the duty among sensors and provides more information from the field. Simulation results verify that the proposed algorithm achieves better reconstruction accuracy and less energy consumption in comparison with state-of-the-art CS reconstruction methods.